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Tıp Fakültesi Öğrencilerinde COVID-19 Süresince Hissedilen Lokomotor Sistem Ağrısının Değerlendirilmesi

2025· article· W7117124591 on OpenAlexaboutno aff
Sibel Ateşoğlu Karabaş, Selma Solgun

Bibliographic record

VenueBlack Sea Journal of Health Science · 2025
Typearticle
Language
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsProcess (computing)Work (physics)Identification (biology)Table (database)

Abstract

fetched live from OpenAlex

Çalışmada tıp fakültesi öğrencilerinin COVID-19’a yakalandıkları süre içerisinde yaşadıkları kas-iskelet sistemi ağrılarının tanımı, ağrı lokalizasyonu ve şiddetinin tanımlanması amaçlanmıştır. 204 kişi çalışmaya gönüllü olarak dahil olmuştur. Ağrı şiddetinin derecelendirilmesi için “Genişletilmiş Nordic Kas İskelet Sistemi Anketi” ve ağrının tanımlanması için “McGill Ağrı Ölçeği Kısa Formu” kullanılmıştır. Çalışmaya katılan 204 gönüllüden %36.8’i erkek ve %63.2’si kadındı. “Genişletilmiş Nordic Kas İskelet Sistemi Anketi” sonuçlarına göre ençok 5.10±3.22 ile baş ağrısı, 4.85±3.55 ile sırt ağrısı ve 4.52±3.57 ile bel ağrısı olduğu tespit edilmiştir. Mevcut ağrıların kadınlarda erkeklerden daha fazla olduğu belirlenmiştir (p

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.786
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.006
Science and technology studies0.0020.003
Scholarly communication0.0000.001
Open science0.0040.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.047
GPT teacher head0.394
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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